https://orcid.org/0000-0002-7629-6843
Figures
Abstract
Introduction
Peptides (B-cell epitopes) have broad applications in disease diagnosis and surveillance of pathogen exposure. In this framework, we present a pilot study to design and produce a peptide microarray for the integrated surveillance of neglected tropical diseases. The peptide microarray was evaluated against peptides derived from Ascaris lumbricoides, Necator americanus, Schistosoma haematobium, Schistosoma mansoni, Trichuris trichiura, Bacillus anthracis, Mycobacterium leprae, Wuchereria bancrofti, Rabies lyssavirus, Chlamydia trachomatis and Trypanosoma brucei.
Methods
S. haematobium was diagnosed using the urine filtration technique. S. mansoni, A. lumbricoides, N. americanus and T. trichiura were diagnosed using the Kato Katz and formal ether concentration techniques. Immunogenic peptides were retrieved from the Tackling Infection to Benefit Africa infectious diseases epitope microarray. Further peptides were predicted using ABCpred. IgG and IgM reactivity against the derived peptides were evaluated using peptide microarray multiplex immunoassays. Positive response was defined as fluorescence intensity ≥ 500 fluorescence units. Immunodominant peptides were identified using color-coded heat maps and bar graphs reflecting the obtained fluorescence signal intensities. Receiver Operating Characteristic analysis and Mann-Whitney-U test were performed to determine the diagnostic validity of the peptides.
Results
Species-specific responses with at least one peptide derived from each NTD pathogen were observed. The reactive peptides included; for S. haematobium, XP_035588858.1-206-220 and XP_035588858.1-206-220 immunodominant for IgG and IgM respectively, for S. mansoni, P20287.1-58-72 immunodominant for both antibodies and for T. trichiura, CDW52482.1-326-340 immunodominant for IgG and CDW57769.1-2017-2031 and CDW57769.1-1518-1532 immunodominant for IgM. According to ROC analysis most of the peptides selected were inaccurate; with AUC < 0.5. Some peptides had AUC values ranging from 0.5 to 0.5875 for both IgM and IgG suggesting no discrimination.
Conclusion
Multiplex peptide microarrays are a valuable tool for integrated NTDs surveillance and for screening parasites exposure in endemic areas. Species sero-reactivity observed in the study maybe indicative of exposure to the different NTDs parasites. However, although peptides with the least cross reactivity were selected there is need to validate the sero-reactivity with recombinant antigens and immune-blotting techniques such as western blotting.
Citation: Vengesai A, Naicker T, Midzi H, Kasambala M, Mduluza-Jokonya TL, Rusakaniko S, et al. (2022) Multiplex peptide microarray profiling of antibody reactivity against neglected tropical diseases derived B-cell epitopes for serodiagnosis in Zimbabwe. PLoS ONE 17(7): e0271916. https://doi.org/10.1371/journal.pone.0271916
Editor: George Vousden, Public Library of Science, UNITED KINGDOM
Received: July 22, 2021; Accepted: July 8, 2022; Published: July 22, 2022
Copyright: © 2022 Vengesai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All the relevant data are within the manuscript and its Supporting Information files.
Funding: This research was commissioned by the National Institute for Health Research (NIHR) Global Health Research programme (16/136/33) using UK aid from the UK Government. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This publication was also supported by a grant from the United States Agency for International Development (USAID) and UK aid from the British people (UK aid) through the Coalition for Operational Research on Neglected Tropical Diseases (COR-NTD) and administered by the African Research Network for Neglected Tropical Diseases (ARNTD). Its contents are solely the responsibility of the author(s) and do not necessarily represent the views of USAID, UK aid, COR-NTD or the ARNTD.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Neglected Tropical Diseases (NTDs) are a group of 20 bacterial, parasitic and viral chronic infectious diseases that affect over 1.7 billion people globally and are particularly endemic to the tropical and subtropical regions [1, 2]. They include, schistosomiasis (mainly caused by Schistosoma haematobium, Schistosoma mansoni and Schistosoma japonicum), soil-transmitted helminthiasis (STH) (commonly caused by Ascaris lumbricoides, Necator americanus and Trichuris trichiura), lymphatic filariasis (caused by Wuchereria bancrofti), blinding trachoma (caused by Chlamydia trachomatis), leprosy (caused by Mycobacterium leprae) and Human African trypanosomiasis (caused Trypanosoma brucei) [1–4]. NTDs also include zoonotic diseases such as rabies (caused by Rabies lyssavirus) and anthrax (caused by Bacillus anthracis) [5].
NTDs are intimately related to poverty and they tend to cluster in the same poor populations [6, 7]. Zimbabwe which is in the Southern region of Africa is endemic to four of the most common NTDs; Schistosomiasis, STH, lymphatic filariasis and trachoma. In 2016 nearly 10 million Zimbabweans required preventative chemotherapy for at least 1 NTD [8]. NTDs mapping results showed that of the 63 districts in Zimbabwe, 56 are endemic for schistosomiasis, 47 are endemic for STH and 39 for lymphatic filariasis [9, 10].
Engels and Savioli suggested that there is need for an integrated approach to eradicate NTDs [7]. Advances in serological multiplex immunoassays have created enormous potential for large-scale, integrated NTDs surveillance [11]. The parallel detection of antibodies has a wide range of potential applications in the diagnosis and surveillance of NTDs as well as in epitope mapping studies, therapeutics and vaccines development [12, 13]. Peptide microarrays provide rapid and high-throughput immunoassay platforms for the simultaneous identification of B-cell epitopes derived from different NTDs parasites. B-cell epitopes have broad applications in the development of peptide based vaccines, in NTDs diagnosis and surveillance of pathogen exposure [14].
With this background, we present a pilot study to design and produce a peptide microarray in a laser-printer based approach and validate the microarray using human serum and plasma samples from three rural districts in Zimbabwe. The peptide microarray carried a panel of fifty-one, 9–18 amino acids B-cell epitopes derived from A. lumbricoides, N. americanus, S. haematobium, S. mansoni, T. trichiura, B. anthracis, M. Leprae, W. bancrofti, Rabies lyssavirus, C. trachomatis and T. brucei. Peptide microarray immunoassays were also established to evaluate the diagnostic performance of peptides derived from A. lumbricoides, N. americanus, S. haematobium, S. mansoni and T. trichiura.
Materials and methods
Ethical approval
Ethical approval was obtained from the Medical Research Council of Zimbabwe (MCRZ/A/2571 and MRCZ/A/2443). Permission to conduct the study in the districts was granted by the Provincial Medical Directors, District Medical Officers, councillors, and village headmen. Participants provided written consent prior to recruitment. Parents and guardians provided written consent for children after the children had given their assent. Prior to recruitment study objectives were explained to the participants, parents and guardians in both Shona and English languages.
Study area and population
Villagers of all age groups living in NTDs endemic areas were purposively selected for the cross-sectional study. The villagers were from Shamva, Murewa and Makoni rural districts in Zimbabwe. Shamva and Murewa rural districts are located in Mashonaland Central province (31°40′0” E longitude and 17°10′0” S latitude) and Mashonaland East province (17°38′49″S latitude and 31°46′39″E longitude) in northeastern Zimbabwe respectively, whilst Makoni district is located in Manicaland province in eastern Zimbabwe (18°32’09.2"S latitude and 32°07’18.9"E longitude). The prevalence’s of S. haematobium, S. mansoni, T. trichiura, N. americanus and A. lumbricoides in the study provinces presented in Table 1 were reported by Midzi et al., 2014 [9]. To be included in the study population villagers were required to provide three urine samples collected on consecutive days, one stool and one blood sample.
Parasitological examination
Urine and stool specimens were collected between 10:00 am and 14:00 pm for optimal egg passage necessary for diagnosis of schistosomiasis and STH. The samples were placed in wooden boxes away from sunlight until they were processed and examined. S. haematobium was diagnosed by the microscopic examination of urine for parasites eggs using the urine filtration technique. The technique was repeated for three consecutive days in order to avoid misdiagnosis due to day-to-day variation in egg excretion [15].
Stool samples were examined for the ova of T. trichiura, N. americanus, A. lumbricoides and S. mansoni using the Kato-Katz technique and the formal ether concentration technique [16, 17]. Participants were classified as infected if at least one parasitic egg was detected. Participants who tested positive for schistosomiasis and STH were referred to the nearest health centres for treatment. Healthy non-infected individuals from the NTDs endemic areas without parasite infections were considered as the negative control group. It is noteworthy that no parasitology diagnosis was conducted for B. anthracis, M. Leprae, W. bancrofti, R. lyssavirus, C. trachomatis and T. brucei.
Peptide selection
The immunogenic peptides (B-cell epitopes) were retrieved from the PEPperPRINT and TIBA infectious disease epitope microarray databases. For novel peptides, a literature search for pathogen proteins was conducted in PubMed. The search focused on proteins found on the surface of the pathogens and secretory or excretory proteins. Protein sequences were then obtained either from NCBI (https://www.ncbi.nlm.nih.gov/) or Uniprot (https://www.uniprot.org/) protein databases. The prediction of linear B-cell epitopes on selected protein sequences was done using a bioinformatics tool ABCpred [14]. The NCBI Protein BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi select Protein BLAST) bioinformatics tool was then used to select the peptides with the least cross-reactive. Predicted peptides with the ABCpred highest rank and with the least cross-reactivity with peptides from other human pathogens or proteins were selected for the study.
Peptide microarray design and layout
The peptide microarray was customer designed to include three to five 9aa-18aa peptides derived from each pathogen and was generated in a laser-printer based approach by PEPperPRINT GmbH (Heidelberg, Germany) (https://www.pepperprint.com/). The peptide microarray contained 16 identical sub-arrays (copies) with 260 peptide positions on each sub-array. Fifty-one duplicate NTDs peptides (details given in Table 2) were printed with random distribution across each sub-array. Each sub-array was framed by flag anti-polio (KEVPALTAVETGAT, 3 spots) and flag anti-HA (hemagglutinin glycoprotein of influenza virus) (YPYDVPDYAG, 3 spots) as a quality control measurement. Each sub-array was also framed by glycine spacers (G, 7 spots) as negative controls. See Fig 1 for details on peptide microarray layout.
(A) Microarray peptide content, in black are 130 selected peptides printed with random distribution in duplicate to give 260 spots and in green and red are 3 HA and 3 Polio internal control spots. (B) Peptide microarray layout with 16 identical array copies (subarrays).
Peptide microarrays immunoassays
The immunoassays were performed in 3/16-well PEPperCHIP® Incubation Trays (PEPperPRINT GmbH, Germany), which allowed for the subdivision of the peptide microarray substrate glass slide into 16 separate incubation wells for each slide. The immunoassays consisted of two steps on the same microarray: the pre-incubation step for identifying false positive signals by binding of the fluorescently labelled secondary antibody followed by the main incubation with serum and the secondary antibodies.
Each step involved pre-swelling of the peptide microarrays with washing buffer (PBS, pH 7.4 with 0.05% Tween 20) for 10 minutes, followed by incubation with blocking buffer (Rockland blocking buffer MB-070) for 30 minutes. Initially the peptide microarrays were incubated with secondary antibodies [Goat anti-human IgG (Fc) DyLight680 (0.1 μg/ml) and goat anti-human IgM (μ chain) DyLight800 (0.2 μg/ml)] and control antibodies [Mouse monoclonal anti-HA DyLight800 (0.5 μg/ml)] diluted in incubation buffer (washing buffer with 10% blocking buffer) at room temperature for 45 minutes. In the main step the peptide microarrays were incubated with serum or plasma diluted 1:250 in incubation buffer for 16 h at 4°C and 140 rpm orbital shaking followed by incubation with the secondary antibodies. After each incubation step the peptide microarrays were washed three times with washing buffer for 10 seconds. The peptide microarrays were scanned using an LI-COR Odyssey Imaging System; scanning offset 0.65 mm, resolution 21 μm, scanning intensities of 7/7 (red = 680 nm / green = 800 nm). To ensure that all microarrays were responding correctly, all steps were repeated with the Cy3-conjugated anti-HA control antibody and Cy3-conjugated anti-polio control antibodies.
Prior data quantification, all the peptide microarray scans were visually assessed for possible assay or microarray artifacts which would affect the experimental outcome. If required, the assays with individual samples were repeated on new peptide microarrays. For technical reasons, read-out for repeated samples was carried out with an Innopsys InnoScan 710-IR microarray scanner with matching scanner settings.
Image analysis and spot intensity quantification
Quantification of spot intensities was based on 16-bit gray scale tiff files. Microarray image analysis was done with PepSlide® Analyzer (SICASYS Software GmbH; Heidelberg, Germany) and resulted in raw data CSV files for each sample (green = 800 nm = IgM staining, red = 680 nm = IgG staining). A PEPperPRINT software algorithm calculated averaged median foreground intensities (foreground-background signal) and spot-to-spot deviations of spot duplicates and assembled the outcome in summary files. For duplicate spots a maximum spot-to-spot deviation of 50% was tolerated, otherwise the corresponding intensity value was zeroed.
Statistical analysis
Age was expressed as median and interquartile range (IQR). The data set (S1 File) used for statistical analysis of the peptide microarray results and for generation of all heat map presentations were based on fluorescence intensity. Duplicate fluorescence values were averaged in Microsoft excel 2013. Bar graphs were also drawn using Microsoft excel 2013.
Heat maps were generated online using Morpheus heat map widget (https://software.broadinstitute.org/morpheus/). Non-parametric statistical methods (Mann-Whitney-U test and nonparametric ROC analysis) were used for data analysis. Univariate comparisons of two independent groups were done using the Mann-Whitney-U test in SPSS 16.0. Area under the ROC curve (AUC) was measured for each peptide using STATA/MP16.0. P-values lower than 0.05 were considered statistically significant.
Antibody reactivity and discrimination of infection by detection of immunodominant epitopes
A positive peptide response was defined as fluorescence intensity ≥ 500 Relative Fluorescent Units (RFU) for both IgG and IgM against each peptide [18]. Immunodominant peptides were identified using color-coded heat maps and bar graphs reflecting obtained fluorescence signal intensities. The ability of peptides to distinguish between the infected and uninfected groups was statistically evaluated using the Mann-Whitney-U test. Diagnostic accuracy of the peptides was evaluated by ROC curve analysis and AUC was calculated to assess the overall diagnostic performance of peptide (S1 Table). Results of antibody reactivity against the peptide microarray internal quality controls spots (KEVPALTAVETGAT, YPYDVPDYAG and G spots) has not been reported in the manuscript but are presented in S2 Table.
Results
Demography and parasitology data
The study consisted of 170 participants of which 49.1% were males, of all age groups with a median age of 11 (interquartile range 5.25–30.00). Madziwa had 74.7% (127) of the participants while the participants in Murewa and Makoni were 17.1% (29) and 8.2% (14) respectively. Among the villagers included in the study, 4 were infected with A. lumbricoides, 14 with N. americanus, 61 with S. haematobium, 44 with S. mansoni and 6 with T. trichiura whilst 60 were uninfected and were used as controls during analysis.
Antibody reactivity and discrimination of Ascaris lumbricoides infection
Ascaris lumbricoides derived peptides were reactive with IgG except peptide CAA60047.1-553-568 (TMKIYARDQGGIHNPP) which did not react with neither Ascaris lumbricoides infected nor uninfected samples. Peptide ACJ03764.1-3852-38-52 (KQIITGAPDKTDAEI) gave the highest response with a fluorescence intensity of 13563.25 RFU with sera from the Ascaris lumbricoides infected group. For IgM, all Ascaris lumbricoides derived peptides were reactive with at least one sera from either the Ascaris lumbricoides infected or uninfected group. In contrast with IgG peptide ACJ03763.1-50-64 (TDPEIEADIDIAFVAK) gave the highest fluorescence intensity 2806.5 RFU. Looking at the heat maps for Ascaris lumbricoides derived peptide there was no immunodominant peptide (S1 Fig). None of the peptides showed a clear discrimination between the Ascaris Lumbricoides infected and uninfected group (Table 3).
Antibody reactivity and discrimination of Necator americanus infection
Peptide AAP41952.1-180-192 (AGNMMGKDIYEKG) was the only reactive peptide for IgM reacting with 6 samples from the negative control group with highest response being 2112. 5 RFU. For IgG no reactivity was observed with fluorescence intensity less than 400 RFU for all the Necator americanus derived peptide in the Necator americanus infected and uninfected groups (S1 Fig).
Like the heat maps for Ascaris lumbricoides derived peptides there was no immunodominant peptide for the Necator americanus peptides and none of the peptides showed a clear discrimination between the Necator americanus infected and uninfected groups (Table 3).
Antibody reactivity and discrimination of S. haematobium infection
S. haematobium derived peptides were all reactive with IgG with high fluorescence intensities observed in the infected group compared to the uninfected group across all the peptides. Peptide XP_012799745.1-16-30 (SFLEMDADNNEEMIDK) gave the highest response 8576 RFU and by observing the heat maps XP_035588858.1-206-220 (EDSDEDDSTVYEVAM) appeared to be the immunodominant peptide for the S. haematobium derived peptides. Peptide XP_035588858.1-206-220 showed discrimination between the S. haematobium infected and uninfected group p<0.037, however it had an AUC of 0.5777417 (S1 Fig). Likewise, all S. haematobium derived peptides reacted with IgM with high fluorescence intensities observed in the infected group compared to the uninfected group across all peptides. Peptide XP_035588858.1-206-220 (EDSDEDDSTVYEVAM) was observed to be the immunodominant peptide and it gave the highest response of 12610 RFU. However, none of the peptides showed a clear discrimination between infected and uninfected groups including XP_035588858.1-206-220 (Table 3).
Antibody reactivity and discrimination of S. mansoni infection
Peptide microarray technology exhibited levels of IgM reactivity against peptides derived from S. mansoni antigens for both S. mansoni infected and uninfected groups, with the exception of AAA29903.1-222-237 which did not react with the S. mansoni uninfected group. For IgG the technology exhibited reactivity for three peptides; P09792.1-29-43 (VDYEDERISFQDWPK) (reacting with 3 samples from the uninfected group), P20287.1-58-72 (GEVSTENGKLKVNGK) (reacting with 1 sample from the uninfected group) and AAA29903.1-222-237 (KSDNQIKAVPASQAL) (reacting with 1 sample from the infected group) with RFU values of 1482.25, 1144 and 532.5, respectively. Examination of the heat maps revealed that peptide P20287.1-58-72 (GEVSTENGKLKVNGK) was the immunodominant peptide for both IgG and IgM (S1 Fig). However, none of the peptides showed a clear discrimination between S. mansoni infected and the uninfected groups (Table 3).
Antibody reactivity and discrimination of Trichuris trichiura infection
Peptide CDW57769.1-659-673 (DNLIKARTNVFAVNK) was the only Trichuris trichiura derived peptide that was not reactive with IgG and peptide CDW52482.1-326-340 (TNEVWEAWTILDDYI) gave the highest RFU value of 8572 with sera from the Trichuris trichiura uninfected group. For IgM, all the peptides showed immunoreactivity with fluorescence intensities above than 500 RFU for all the peptide in the Trichuris trichiura infected and uninfected groups. Visual inspection of the heat maps showed that peptide CDW52482.1-326-340 was immunodominant for IgG and peptide CDW57769.1-2017-2031 (RPEYKDKECYLEHDE) and peptide CDW57769.1-1518-1532 (VRYESFRVAADDFLD) were immunodominant for IgM (S1 Fig). None of the peptides showed a clear discrimination between the Trichuris trichiura infected and uninfected group (Table 3).
Antibody reactivity against peptides derived from Bacillus anthracis proteins
Peptide microarray technology showed IgG reactivity against two peptides derived from Bacillus anthracis antigens, WP_001022096.1-35-44 (GGHGDVGMHV) and WP_040119992.1-736-358 (IINPSENGDTSTNGIKK) with RFU values of 1034 and 665.25, respectively. For IgM all the peptides were responsive and WP_001022096.1-35-44 gave the highest fluorescence intensity value of 2502.5 RFU. The heat maps indicated that peptide WP_001022096.1-35-44 appeared to be immunodominant for pathogens (S2 Fig).
Antibody reactivity against peptides derived from Mycobacterium leprae proteins
Peptide microarray technology showed that all Mycobacterium leprae derived peptides were responsive with at least one plasma or serum sample for both IgG and IgM. Peptide WP_010907696.1-11-25 (DKILVQAGEAETMTP) gave the highest fluorescence intensity for both IgG (2869 RFU) and IgM (7803.5 RFU). The heat maps indicated that peptide CAA37572.1-10-24 (EIDLTNKNAAKLRGD) was immunodominant for both IgG and IgM (S2 Fig).
Antibody reactivity against peptides derived from Wuchereria bancrofti proteins
Peptide microarray technology showed that both IgG and IgM were reactive with Wuchereria bancrofti derived peptides except for AAC35355.1-55-68 (EVVETDGKKKECSS) which had a fluorescent intensity of 494 RFU for IgG. Peptide AAC35355.1-46-60 (GGDEYVTKGEVVETD) gave the highest fluorescence intensity for both IgG (6680.75 RFU) and IgM (3111 RFU). The heat maps indicate that peptide AAC35355.1-46-60 was also immunodominant for both IgG and IgM (S2 Fig).
Antibody reactivity against peptides derived from Rabies lyssavirus proteins
Peptide microarray technology showed that only one Rabies lyssavirus peptide BAJ04981.1-12-26 (QYEYKYPAIKDLKKP) was reactive with IgG with a fluorescence intensity of 1048 RFU. For IgM all peptides were reactive and QEJ74712.1-99-113 (NVGVQIVRQMRSGER) gave the highest fluorescence intensity (1872 RFU). The heat maps indicate that peptide QEJ74712.1-99-113 was also immunodominant for IgM (S2 Fig).
Antibody reactivity against peptides derived from Chlamydia trachomatis proteins
Peptide microarray technology exhibited levels of IgM reactivity against all peptides derived from Chlymadia trachomatis. For IgG the technology exhibited reactivity against three peptides; P19542.1-261-270 (TKDASIDYHE), AAA23156.1-73-81 (VLKTDVNKE) and AAA23156.1-381-390 (TRLIDERAAH) with RFU values of 598.25, 1324.5 and 908.75 respectively. Peptide P19542.1-261-270 exhibited the highest response for IgM with fluorescence intensity of 5808 RFU. Peptide P19542.1-261-270 was also immunodominant for both IgG and IgM compared to the other peptides (S2 Fig).
Antibody reactivity against peptides derived from Trypanosoma brucei proteins
Peptide microarray technology showed that only one Trypanosoma brucei derived peptide XP_011774209.1-132-145 (SAIHATKTVDIKAL) was reactive with IgG with a fluorescence intensity of 1629.5 RFU. For IgM the technology exhibited reactivity against three peptides; XP_844815.1-369-382 (SSKIKESKVILMAV) (reacting with one serum sample), APD74596.1-96-110 (ENREKWEADKKLIVA) (reacting with 5 serum samples) and CAC33895.1-78-92 (ETDFKATADDNKKPQ) with RFU values of 2069.5, 1266.25 and 822.25 respectively. Peptide XP_844815.1-369-382 exhibited the highest response for IgM with fluorescence intensity of 2069.5 RFU. Peptide XP_844815.1-369-382 was also immunodominant for both IgG and IgM compared to the other peptides (S2 Fig).
Discussion
In a time of fierce competition for global health funding, surveillance of a single infection is difficult to defend as a priority [19]. Accurate surveillance of multiple-infections enable public health officials to identify opportunities for implementation of integrated control programs [20]. NTDs multiplex peptide microarray tools could represent an integrated surveillance platform that could be used for monitoring exposure and for disease diagnosis. With this background, B-cell linear epitopes derived from the antigenic proteins of Ascaris lumbricoides, Necator americanus, S. haematobium, S. mansoni, Trichuris trichiura, Bacillus anthracis, Mycobacterium leprae, Wuchereria Bancrofti, Rabies lyssavirus, Chlamydia trachomatis and Trypanosoma brucei were selected for peptide microarray technology. Plasma and serum samples from NTDs endemic areas were evaluated on the peptide microarray to detect IgG and IgM antibodies as described in the experimental section. Species sero-reactivity observed with both IgG and IgM was indicative of exposure to the different NTDs parasites antigens in Murewa, Makoni and Shamva rural districts.
As a typical finding of peptide microarray immunoassays, the signal distribution was highly skewed as was described by Hecker and colleagues [21]; many peptides were detected with high signals for several samples, with no obvious difference between the infected and uninfected groups. The results were in agreement with Odegaard and Hsieh, 2014 who demonstrated that exposure is universal in schistosomiasis endemic areas as defined by the ubiquity of schistosomiasis specific antibodies [22].
Comparative studies between IgM and IgG antibody detection in endemic areas have showed significant differences in their diagnostic capabilities, demonstrating a higher IgM detection in a low endemic setting, without extensive knowledge of the particular infective conditions in studied individuals [23]. In the present study AUC was chosen to summarize the overall diagnostic accuracy of IgG and IgM for Ascaris lumbricoides, Necator americanus, S. haematobium, S. mansoni and Trichuris trichiura derived peptides. AUC was chosen because it is a classification-threshold-invariant that measures the quality of model predictions irrespective of classification threshold. AUC takes values from 0 to 1, where a value of 0 indicates a perfectly inaccurate test and a value of 1 reflects a perfectly accurate test. According to AUC values most of the peptides selected were inaccurate (that is they falsely diagnosed villagers infected or uninfected with disease) with AUC values less than 0.5. Two peptides CAC00543.1-123-135 and AAP41952.1-194-206 were perfectly inaccurate (100% wrong) in predicting villagers with disease and those without disease. Some peptides had AUC values ranging from 0.5 to 0.5875 for both IgM and IgG suggesting no discrimination (that is no ability to diagnose villagers with and without the disease or condition based on the peptides) [24, 25].
Justifications for the lack of discrimination between the infected and uninfected groups by the peptides selected in the study are multi-fold. Just like most antibody detection assays peptide microarray immunoassays does not allow for differentiation of acute or previous infections nor discrimination between persisting antibodies and reinfection. Several studies have demonstrated that antibody detection test may not differentiate between active, prior infection or re-infection because antibodies may persist for many months to years after successful treatment in most of the NTDs [26, 27]. Hinz and colleagues (2017) suggested that remaining or increasing antibody level limits the usefulness of serological tests. In some individuals negative or intermediate results may be misleading due to a low level of antibody response and late or absent seroconversion or an age-dependent decreasing antibody response in people from endemic regions, resulting from cumulative exposure to schistosomes [27].
Commonly used diagnostic methods for schistosomiasis and STH have low sensitivity for the detection of light infections; and many light infections are missed due to absence of eggs in urine and stool specimens [28–31]. Compared to parasitological diagnosis, serology provides more sensitive tools for the diagnosis of helminths, especially in infections with low intensity [27]. The above-mentioned points may have resulted in the failure of the peptides in diagnosing schistosomiasis and STHs.
Limitations and recommendations
Sero-reactivity of at least one peptide derived from each NTD with at least one sample was indicative of the presence of these NTDs in the study population, however, we would need to include sera from uninfected individual from non-endemic areas with no history of exposure to determine the seroprevalence of the NTDs.
A clear limitation of conventional peptide microarrays is their restriction to linear protein epitopes, whereas conformational epitope antibody recognition cannot be identified [32]. Detection of antibodies recognizing all potential epitopes whether linear, conformational or carbohydrate or LPS is a key requirement to comprehensively profile the humoral immune response [33]. Studies have shown that sera of individuals infected with helminths such as S. haematobium and S. mansoni contain IgG and IgM antibodies against defined carbohydrate epitopes [34, 35]. This meant that the peptide microarray technology could not detect antibodies that bind non-protein epitopes. In future peptide microarray studies, we recommend including both linear and conformational protein epitope peptide for evaluation.
The peptide microarray technology described in this study is too complex and expensive for routine clinical microbiology. We recommend that the peptides established by the technology be transferred to a wide range of platforms including, enzyme-linked immunosorbent assay, radioimmunoassay, lateral flow, western blot, and bead-based assays, where they may facilitate diagnostics, epidemiology, and vaccinology. Lateral flow assay can be used in field settings in low resource countries.
Conclusion
Although the selected peptides had poor diagnostic performances, species-specific sero-reactivity was indicative of exposure to the different parasitic antigens in the study population. Peptide microarray immunoassay demonstrated that more individuals in Shamva, Murewa and Makoni rural districts in Zimbabwe were exposed to schistosomiasis and STHs than the expected infection prevalence. Peptide microarrays maybe valuable tools for integrated NTDs surveillance and for screening parasites exposure in endemic areas.
Supporting information
S1 Fig. Heat maps and bar graphs generated from peptide microarray data for A. lumbricoides, N. americanus, S. haematobium, S. mansoni and T. trichiura.
Samples are arranged in rows and infection status shown on the left key. Peptides shown by their protein accession number and sequence position are arranged in columns. Bar graphs representing the peptide reactivity for each serum and plasma in both the infected and uninfected groups in the study.
https://doi.org/10.1371/journal.pone.0271916.s001
(PDF)
S2 Fig. Heat maps and bar graphs generated from peptide microarray data for B. anthracis, M. Leprae, W. bancrofti, Rabies lyssavirus, C. trachomatis and T. brucei.
Samples are arranged in rows and infection status shown on the left key. Peptides shown by their protein accession number and sequence position are arranged in columns. Bar graphs representing the peptide reactivity for each serum and plasma in both the infected and uninfected groups in the study.
https://doi.org/10.1371/journal.pone.0271916.s002
(PDF)
S1 Table. Area under the ROC curve and the diagnostic performance of each peptide.
https://doi.org/10.1371/journal.pone.0271916.s003
(PDF)
S2 Table. Antibody reactivity against the peptide microarray internal quality control spots.
https://doi.org/10.1371/journal.pone.0271916.s004
(XLSX)
S1 File. Peptide microarray immunoassay data set.
https://doi.org/10.1371/journal.pone.0271916.s005
(RAR)
Acknowledgments
We thank the communities of Shamva, Murewa and Makoni rural districts for their participation and support in the study.
References
- 1.
Neglected tropical diseases. https://www.who.int/news-room/q-a-detail/neglected-tropical-diseases
- 2.
CDC—Neglected Tropical Diseases—Diseases. https://www.cdc.gov/globalhealth/ntd/diseases/index.html
- 3. Mitra AK, Mawson AR. Neglected tropical diseases: Epidemiology and global burden [Internet]. Vol. 2, Tropical Medicine and Infectious Disease. MDPI AG; 2017: www.mdpi.com/journal/tropicalmed pmid:30270893
- 4. Da Silva Santos L, Wolff H, Chappuis F, Albajar-Viñas P, Vitoria M, Tran NT, et al. Coinfections between persistent parasitic neglected tropical diseases and viral infections among prisoners from Sub-Saharan Africa and Latin America. J Trop Med. 2018;2018. pmid:30532789
- 5. WHO | Neglected zoonotic diseases: https://www.who.int/neglected_diseases/zoonoses/infections_more/en/
- 6. Engels D, Zhou XN. Neglected tropical diseases: An effective global response to local poverty-related disease priorities [Internet]. Vol. 9, Infectious Diseases of Poverty. BioMed Central Ltd.; 2020 p. 1–9: https://doi.org/10.1186/s40249-020-0630-9
- 7. Engels D, Savioli L. Reconsidering the underestimated burden caused by neglected tropical diseases. Trends Parasitol 2006;22(8):363–6.: https://pubmed.ncbi.nlm.nih.gov/16798088/ pmid:16798088
- 8.
Zimbabwe | ESPEN 2021: https://espen.afro.who.int/countries/zimbabwe
- 9. Midzi N, Mduluza T, Chimbari MJ, Tshuma C, Charimari L, Mhlanga G, et al. Distribution of Schistosomiasis and Soil Transmitted Helminthiasis in Zimbabwe: Towards a National Plan of Action for Control and Elimination. PLoS Negl Trop Dis 2014;8(8):e3014: http://dx.plos.org/10.1371/journal.pntd.0003014 pmid:25121489
- 10. Phiri I, Manangazira P, Macleod CK, Mduluza T, Dhobbie T, Chaora SG, et al. The Burden of and Risk Factors for Trachoma in Selected Districts of Zimbabwe: Results of 16 Population-Based Prevalence Surveys. Ophthalmic Epidemiol 2018;25(sup1):181–91: http://www.ncbi.nlm.nih.gov/pubmed/28532208 pmid:28532208
- 11. Arnold BF, der Laan MJ va., Hubbard AE, Steel C, Kubofcik J, Hamlin KL, et al. Measuring changes in transmission of neglected tropical diseases, malaria, and enteric pathogens from quantitative antibody levels. PLoS Negl Trop Dis 2017;11(5):1–20: https://doi.org/10.1101/106708
- 12. Sachse K, Rahman KS, Schnee C, Müller E, Peisker M, Schumacher T, et al. A novel synthetic peptide microarray assay detects Chlamydia species-specific antibodies in animal and human sera. Sci Rep 2018;8(1):4701.www.nature.com/scientificreports/
- 13. Duburcq X, Olivier C, Desmet R, Halasa M, Carion O, Grandidier B, et al. Polypeptide Semicarbazide Glass Slide Microarrays: Characterization and Comparison with Amine Slides in Serodetection Studies. Bioconjug Chem2004;15(2):317–25.: https://pubs.acs.org/doi/abs/10.1021/bc034118r pmid:15025527
- 14. Saha S, Raghava GPS. Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins Struct Funct Bioinforma 2006;65(1):40–8: http://doi.wiley.com/10.1002/prot.21078
- 15. Mott KE, Baltes R, Bambagha J, Baldassini B. Field studies of a reusable polyamide filter for detection of schistosoma haematobium eggs by urine filtration. Tropenmed Parasitol,1982;33(4):227–8: https://europepmc.org/article/med/7164164 pmid:7164164
- 16. George S, Geldhof P, Albonico M, Ame SM, Bethony JM, Engels D, et al. Molecular speciation of soil-transmitted helminths egg isolates collected during six drug efficacy trials in endemic countries. Trans R Soc Trop Med Hyg. 2016;110(11):657. pmid:28100811
- 17. Knopp S, Becker SL, Ingram KJ, Keiser J, Utzinger J. Diagnosis and treatment of schistosomiasis in children in the era of intensified control. Vol. 11, Expert Review of Anti-Infective Therapy. Expert Rev Anti Infect Ther; 2013 p. 1237–58: https://pubmed.ncbi.nlm.nih.gov/24127662/ pmid:24127662
- 18. Schwarz T, Heiss K, Mahendran Y, Casilag F, Kurth F, Sander LE, et al. SARS-CoV-2 Proteome-Wide Analysis Revealed Significant Epitope Signatures in COVID-19 Patients. Front Immunol, 2021;12:765: www.frontiersin.org pmid:33833755
- 19. Lammie PJ, Moss DM, Brook Goodhew E, Hamlin K, Krolewiecki A, West SK, et al. Development of a new platform for neglected tropical disease surveillance. Int J Parasitol. 2012 Aug 1;42(9):797–800. pmid:22846784
- 20. Njenga SM, Kanyi HM, Arnold BF, Matendechero SH, Onsongo JK, Won KY, et al. Integrated cross-sectional multiplex serosurveillance of IgG antibody responses to parasitic diseases and vaccines in coastal Kenya. Am J Trop Med Hyg, 2020;102(1):164–76: https://osf.io/taknp pmid:31769388
- 21. Hecker M, Fitzner B, Wendt M, Lorenz P, Flechtner K, Steinbeck F, et al. High-density peptide microarray analysis of IgG autoantibody reactivities in serum and cerebrospinal fluid of multiple sclerosis patients. Mol Cell Proteomics, 2016;15(4):1360–80: https://pubmed.ncbi.nlm.nih.gov/26831522/ pmid:26831522
- 22. Odegaard JI, Hsieh MH. Immune responses to Schistosoma haematobium infection [Internet]. Vol. 36, Parasite Immunology. Blackwell Publishing Ltd; 2014. p. 428–38.: https://onlinelibrary.wiley.com/doi/full/10.1111/pim.12084
- 23. Cosenza M, Barrios E, Felibertt P, Castillo-Corujo A, Ochoa G, Velasquez E, et al. IgM and IgG responses in Schistosoma mansoni-infected mice using egg and worm antigens: Does response vary with parasitic burden and phase of infection? Exp Parasitol. 2017 Aug 1;179:36–42. pmid:28655585
- 24. Mandrekar JN. Receiver Operating Characteristic Curve in Diagnostic Test Assessment. Vol. 5, Journal of Thoracic Oncology. 2010.
- 25. Malnati MS, Heltai S, Cosma A, Reitmeir P, Allgayer S, Glashoff RH, et al. A new antigen scanning strategy for monitoring HIV-1 specific T-cell immune responses. J Immunol Methods, 2012;375(1–2):46–56: https://pubmed-ncbi-nlm-nih-gov.ukzn.idm.oclc.org/21963950/ pmid:21963950
- 26. Martelli G, Di Girolamo C, Zammarchi L, Angheben A, Morandi M, Tais S, et al. Seroprevalence of five neglected parasitic diseases among immigrants accessing five infectious and tropical diseases units in Italy: a cross-sectional study. Clin Microbiol Infect, 2017;23(5):335.e1-335.e5: http://dx.doi.org/10.1016/j.cmi.2017.02.024 pmid:28259548
- 27.
Hinz R, Schwarz NG, Hahn A, Frickmann H. Serological approaches for the diagnosis of schistosomiasis–A review. Vol. 31, Molecular and Cellular Probes. Academic Press; 2017. p. 2–21.
- 28. Knopp S, Corstjens PLAM, Koukounari A, Cercamondi CI, Ame SM, Ali SM, et al. Sensitivity and Specificity of a Urine Circulating Anodic Antigen Test for the Diagnosis of Schistosoma haematobium in Low Endemic Settings. PLoS Negl Trop Dis, 2015;9(5):e0003752: pmid:25973845
- 29. Fenta A, Hailu T, Alemu M, Nibret E, Amor A, Munshea A. Evaluating the performance of diagnostic methods for soil transmitted helminths in the Amhara National Regional State, Northwest Ethiopia. BMC Infect Dis, 2020;20(1):1–8: https://doi.org/10.1186/s12879-020-05533-2 pmid:33121458
- 30. Nikolay B, Brooker SJ, Pullan RL. Sensitivity of diagnostic tests for human soil-transmitted helminth infections: A meta-analysis in the absence of a true gold standard. Int J Parasitol. 2014 Oct 1;44(11):765–74. pmid:24992655
- 31. Glinz D, Silué KD, Knopp S, Lohourignon LK, Yao KP, Steinmann P, et al. Comparing diagnostic accuracy of Kato-Katz, Koga Agar Plate, Ether-Concentration, and FLOTAC for Schistosoma mansoni and Soil-transmitted helminths. PLoS Negl Trop Dis, 2010;4(7):e754: www.plosntds.org pmid:20651931
- 32. Loeffler FF, Pfeil J, Heiss K. High-density peptide arrays for malaria vaccine development. In: Methods in Molecular Biology. Humana Press Inc. 2016; p. 569–82: https://link.springer.com/protocol/10.1007/978-1-4939-3387-7_32
- 33. Legutki JB, Magee DM, Stafford P, Johnston SA. A general method for characterization of humoral immunity induced by a vaccine or infection. Vaccine, 2010;28(28):4529–37: https://pubmed-ncbi-nlm-nih-gov.ukzn.idm.oclc.org/20450869/ pmid:20450869
- 34. Michelle Yang YY, Van Diepen A, Brzezicka K, Reichardt NC, Hokke CH. Glycan microarray-assisted identification of IgG subclass targets in schistosomiasis. Front Immunol. 2018 Oct 9;9(OCT):2331.
- 35. Van Remoortere A, Van Dam GJ, Hokke CH, Van den Eijnden DH, Van Die I, Deelder AM. Profiles of immunoglobulin M (IgM) and IgG antibodies against defined carbohydrate epitopes in sera of Schistosoma-infected individuals determined by surface plasmon resonance. Infect Immun, 2001;69(4):2396–401: https://pubmed.ncbi.nlm.nih.gov/11254599/ pmid:11254599